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Data mining and predictive modeling of biomolecular network from biomedical literature databases
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/2013
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| Title: | Data mining and predictive modeling of biomolecular network from biomedical literature databases |
| Authors: | Hu, Xiaohua
Wu, Daniel |
| Keywords: | Biomolecular Network
Semisupervised Learning
Scale-Free Network
Information Extraction
Biological Complexes (Communities) |
| Issue Date: | Apr-2007 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Citation: | IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(2): pp. 251-263 . |
| Abstract: | In this paper, we present a novel approach Bio-IEDM (Biomedical Information Extraction and Data Mining) to integrate text
mining and predictive modeling to analyze biomolecular network from biomedical literature databases. Our method consists of two
phases. In phase 1, we discuss a semisupervised efficient learning approach to automatically extract biological relationships such as
protein-protein interaction, protein-gene interaction from the biomedical literature databases to construct the biomolecular network.
Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical
literature based on the discovered patterns. The derived biomolecular network forms a large scale-free network graph. In phase 2, we
present a novel clustering algorithm to analyze the biomolecular network graph to identify biologically meaningful subnetworks
(communities). The clustering algorithm considers the characteristics of the scale-free network graphs and is based on the local
density of the vertex and its neighborhood functions that can be used to find more meaningful clusters with different density level. The
experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical
literature. The integration of data mining and information extraction provides a promising direction for analyzing the biomolecular
network. |
| URI: | http://dx.doi.org/10.1109/TCBB.2007.070211
http://hdl.handle.net/1860/2013 |
| Appears in Collections: | Faculty Research and Publications (IST)
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